Control Design by Extended Linearisation Techniques for a Two Degrees of Freedom Helicopter

Abstract In this paper, a decentralised tracking control based on extended linearisation techniques is presented for a two rotor laboratory helicopter with two degrees of freedom. Employing Lagrange's equations, a nonlinear control-oriented state-space model can be derived. It is rewritten in a quasi-linear form – without any simplifications – with state-dependent matrices as a basis for the decentralised feedforward and feedback control design. The control task consists in tracking accurately desired trajectories for both the azimuth angle and the pitch angle. Due to unmeasurable states as well as uncertainties stemming from both model simplifications at modelling and unknown disturbance torques, an unscented Kalman filter is employed and combined with a discrete-time implementation of the nonlinear control law. The eficiency of the proposed controller is demonstrated by results from an experimental set-up.

[1]  F.R. Rubio,et al.  Control of a laboratory helicopter using switched 2-step feedback linearization , 2004, Proceedings of the 2004 American Control Conference.

[2]  I.Z.M. Darus,et al.  Parametric modelling of a twin rotor system using genetic algorithms , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[3]  Harald Aschemann,et al.  Cascaded backstepping control of a Duocopter including disturbance compensation by unscented Kalman filtering , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[4]  Harald Aschemann,et al.  Multi-variable flatness-based control of a helicopter with two degrees of freedom , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[5]  M. O. Tokhi,et al.  Nonlinear modelling of a twin rotor MIMO system using radial basis function networks , 2000, Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093).

[6]  Michael J. Grimble,et al.  Non-linear predictive control of 2 DOF helicopter model , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[7]  Akbar Rahideh,et al.  Mathematical dynamic modelling of a twin-rotor multiple input-multiple output system , 2007 .

[8]  M.G. Ortega,et al.  A Multivariable Nonlinear H∞Controller for a Laboratory Helicopter , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[9]  Harald Aschemann,et al.  An experimental study on decentralised backstepping approaches for a hydrostatic drive train with unknown disturbances , 2014, 2014 American Control Conference.

[10]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[11]  Harald Aschemann,et al.  An experimental study of extended linearisation approaches for a hydrostatic transmission with unknown disturbances , 2014, 2014 European Control Conference (ECC).